Entropy-Based vs. Similarity-Influenced:
Attribute Selection Methods for Dialogs
Tested on Different Electronic Commerce Domains

Sascha Schmitt1, Philipp Dopichaj1, and Patricia Domnguez-Marn2

1Artificial Intelligence - Knowledge-Based Systems Group
Department of Computer Science, University of Kaiserslautern
67653 Kaiserslautern, Germany
{sschmitt | dopichaj}@informatik.uni-kl.de
2Department of Optimization
ITWM - Fraunhofer Institut fr Techno- und Wirtschaftsmathematik
P.O. Box 3049, 67618 Kaiserslautern, Germany
dominguez@itwm.fhg.de



Abstract. Recent research activities in the field of attribute selection for
carrying on dialogs with on-line customers have focused on entropy-based
approaches that make use of information gain measures. These measures
consider the distribution of attribute values in the case base and are focused on
their ability to reduce dialog length. The implicit knowledge contained in the
similarity measures is neglected. In previous work, we proposed the similarity-influenced 
selection method sim Var, which selects the attributes that induce the
maximum change in similarity distribution amongst the candidate cases,
thereby partitioning the case base into similar and dissimilar cases. In this paper
we present an evaluation of the selection methods using three domains with
distinct characteristics. The comparison of the selection methods is based on the
quality of the dialogs generated. Statistical analysis was used to support the
evaluation results.
References

1.	Auriol, E., Wess, S., Manago, M., Althoff, K.D., Traphner, R.: INRECA. A Seamlessly
Integrated System Based on Inductive Inference and Case-Based Reasoning. In: Veloso,
M., Aamodt, A. (eds.): Case-Based Reasoning Research and Development. Proc. of the 1st
Intemat. Conf. on Case-Based Reasoning, ICCBR95. LNAI 1010, Springer-Verlag (1995)
2.	Bergmann, R., Breen, S., Gker, M., Manago, M., Wess, S.: Developing Industrial Case-Based 
Reasoning Applications. The INRECA-Methodology. LNAI 1612, Springer-Verlag
(1999)
3.	Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-Oriented
Matching:	A New Research Direction for Case-Based Reasoning. In: Vollrath, I., Schmitt,
S., Reimer, U. (eds.): Proc. of the 9th German Workshop on Case-Based Reasoning,
GWCBR01, Baden-Baden, Germany. In: Schnurr, H.-P., Staab, S., Studer, R., Stumme,
G., Sure, Y. (Hrsg.): Professionelles Wissensmanagement. Shaker Verlag (2001)
4.	Cohen, P.R.: Empirical Methods for Artificial Intelligence. The MIT Press (1995)
5.	Cunningham, P., Bergmann, R., Schmitt, S., Traphner, R., Breen, S., Smyth, B.:
WEB SELL: Intelligent Sales Assistants for the World Wide Web. In: Weber, R., Gresse
von Wangenheim, C. (eds.): Proc of the Workshop Program at the 4th International
Conference on Case-Based Reasoning, ICCBR-2001, Vancouver, Canada. Workshop 3:
Case-Based Reasoning in Electronic Commerce (2001)
6.	Cunningham, P., Smyth, B.: A comparison of model-based and incremental case-based
approaches to electronic fault diagnosis. In: Proc. of the Case-Based Reasoning Workshop
at AAAI-94 (1994)
7.	Doyle, M., Cunningham, P.: A Dynamic Approach to Reducing Dialog in On-Line
Decision Guides. In: Blanzieri, E., Protionale, L. (eds.): Advances in Case-Based
Reasoning. Proc. of the 5th European Workshop on Case-Based Reasoning, EWCBR 2000.
LNAI 1898, Springer-Verlag (2000)
8.	Kohlmaier, A., Schmitt, S., Bergmann, R.: A Similarity-based Approach to Selection in
User-Adaptive Sales Dialogs. In: Aha, D.W., Watson, I. (eds.): Case-Based Reasoning
Research and Development. Proc. of the 4th International Conference on Case-Based
Reasoning, ICCBR-2001. LNAI 2080, Springer-Verlag (2001)
9.	Kohlmaier, A., Schmitt, S., Bergmann, R.: Evaluation of a Similarity-based Approach to
Customer-adaptive Electronic Sales Dialogs. In: Weibelzahl, S., Chin, D., Weber, G.
(eds.): Empirical Evaluation of Adaptive Systems. Proc. of the workshop held at the 8th
International Conference on User Modeling (2001)
10.	McSherry, D.: Minimizing Dialog Length in Interactive Case-Based Reasoning. In: Proc.
of the 17th International Joint Conference on Artificial Intelligence, IJCAI-01, Seattle,
USA (2001)
11.	Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)
12.	orenge:dialog. In: orenge: Open Retrieval Engine 3.2 Manual. empolis  knowledge
management. http://www.km.empolis.com/
13.	Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1 (1986)
14.	Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo,
California (1993)
15.	Schmitt, S.: simVar:A Similarity-Influenced Question Selection Criterion for c-Sales
Dialogs. In: Burke, R., Cunningham, P. (eds.): Special Issue on AI Approaches to User
Requirements Elicitation for E-Commerce. Artificial Intelligence Review. To be published
16.	Schmitt, S., Bergmann, R.: A Formal Approach to Dialogs with Online Customers. In:
OKeefe, B., Loebbecke, C., Gricar, J., Pucihar, A., Lenart, G. (eds.): c-Everything: e-
Commerce, e-Government, c-Household, c-Democracy. Proc. of the 14th Bled Electronic
Commerce Conference. Vol. 1: Research (2001)
17.	Smyth, B., Cunningham, P.: A Comparison of Incremental Case-Based Reasoning and
Inductive Learning. In: Proc. of the 2nd European Workshop on Case-Based Reasoning,
EWCBR94, Chantilly, France (1994)
18.	Stoodley, K.D.C., Lewis, T., Stainton, C.L.S.: Applied Statistical Techniques. Halsted
Press (1980)
19.	Wilke, W., Lenz, M., Wess, S.: Intelligent Sales Support with CBR. In: Lenz, M. Bartsch-
Sprl, B., Burkhard, H.D., Wess, S. (eds.): Case-Based Reasoning Technology. From
Foundations to Applications. Springer-Verlag (1998)
20.	Yang, Q., Wun J.: Enhancing the Effectiveness of Interactive Case-Based Reasoning with
Clustering and Decision Forests. In: Aha, D., Muoz-Avila, H. (eds.): Applied Intelligence
Journal, Special Issue on Interactive CBR (1999).
